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chore: fixing kafka in summary

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Daniel Rosel
2026-04-10 08:19:37 +02:00
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paper/src/summary.tex
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@@ -53,10 +53,10 @@ As the number of independent competitive agents $N$ querying the system grows, t
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In order for our research to have grounding in interactions we built a robust e-commerce web-platform.
The architecture of this platform begins with the deployed web-apps posting interaction data to our backend which processes them and stores each ingested interaction into a kafka cluster.
The architecture of this platform begins with the deployed web-apps posting interaction data to our backend which processes them and stores each ingested interaction into a Kafka cluster.
This serves as our data reservoir tracking and associating each interaction with its session and importantly with which experiment it belongs to.
Not only do we track the behavioral interactions, but our pricing provider micro-service, once called by the frontend reports the observed/queried price-product into kafka.
This kafka cluster is subscribed to by our pipeline which is configured on a schedule in Airflow, with the possibility of manual trigger.
Not only do we track the behavioral interactions, but our pricing provider micro-service, once called by the frontend reports the observed/queried price-product into Kafka.
This Kafka cluster is subscribed to by our pipeline which is configured on a schedule in Airflow, with the possibility of manual trigger.
The final stage of the pricing pipeline, submits computed dynamic pricing results into a redis database for quick updates which is then read by the pricing provider and displayed on the webapp.
This is a very generic end-to-end mechanism which is applicable to a variety of different e-commerce tasks.
We intentionally put emphasis on the development of this infrastructure to establish a reproducible framework for interaction and to minimize any noise.